Affiliations: Department of Computer Science and Engineering, The Nothcap University, Sector-23A, Gurugram, India
Correspondence:
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Corresponding author. Arpita Nagpal, Computer Science and Engineering Department, The Nothcap University, Sector-23A, Gurugram, India. E-mail: [email protected].
Abstract: High Dimensional cancer microarray is devilishly challenging while finding the best features for classification. In this paper a new algorithm is proposed based on iterative qualitative mutual information to choose the features that can provide optimal feature set with reliability, stability, and best classification results. It finds the qualitative (i.e. utility) score of each feature with the help of Random Forest algorithm and combines it with mutual information of each feature with its class variable. Adding a qualitative measure along with mutual information can improve the robustness and find redundant features in data. The proposed algorithm has been compared with other representative methods through the ten microarray based cancer datasets in terms of number of features and classification accuracy of three well-known classifiers: Naïve Bayes, IB1 and C4.5. Experimental results show that the proposed approach is effective in producing an optimal feature subset and improves the accuracy of these datasets.
Keywords: Feature selection, microarray, classification, wrapper, filter model, random forest, mutual information
